MLOps Engineer - Implementation

BMW AG
München, Germany
2 months ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Experienced
Experience required
3 years minimum
Working hours
Shift work
Job source

Tech stack

Amazon Web Services AUTomotive Open System Architecture (AUTOSAR) Relational Databases Software Debugging Distributed Computing Environment Python (Programming Language) PostgreSQL Data Ingestion Kubernetes Helm Charts Kubernetes Information Technology Build Tools
+3 more
Machine Learning Operations Terraform Data Pipelines

Job description

orchestration tools: from data ingestion to distributed training, evaluation, model compilation, and deployment-ready artefacts. </li> <li> Furthermore, you engineer petabyte-scale data pipelines that consume domain datasets, transforming raw MDF4 (.mf4) and MCAP log files into training-ready formats. </li> <li> You build tooling for efficient parallel readers, signal extraction, synchronisation of multi-sensor streams, and integration with dataset management platforms for visual QA and curation. </li> <li> Also, you manage experiment tracking, hyperparameter tuning and model registry, enforcing reproducibility, lineage, and approval gates from experiment to production. </li> <li> You develop and maintain model compilation and optimisation pipelines targeting in-vehicle Qualcomm Snapdragon Ride chips and/or NVIDIA automotive SoCs. </li> <li> On top, you operate observability stacks, providing dashboards, data-drift alerts, pipeline SLOs, and log aggregation. </li> </ul> <p>Profil

Requirements

</p> <ul> <li> University degree in Computer Science, Engineering, or a related field. </li> <li> 3-5 years of hands-on ML infrastructure or MLOps experience. </li> <li> Strong Python skills; experience with hermetic build systems (e.g., Bazel) is a plus. </li> <li> Production Kubernetes experience, including deploying and debugging workloads, writing Helm charts, and managing accelerator node pools. </li> <li> Working knowledge of ML pipeline orchestration, experiment tracking, and hyperparameter optimization. </li> <li> Hands-on experience with infrastructure-as-code for AWS (e.g., Terraform) and automotive measurement data, such as MDF4 or MCAP. </li> <li> Comfortable with relational databases (e.g., PostgreSQL) for metadata stores and experience with dataset management tools, functional-safety awareness (ISO 26262), or AUTOSAR Adaptive. </li> </ul> <p>Wir bieten </p> <ul> <li> Challenging projects with which we shape the mobility of tomorrow together. </li> <li> Wide range of

About the company

At the BMW Group, everything begins with passion. It transforms a profession into a vocation. It drives us to continually reinvent mobility and bring innovative ideas to the roads. Enthusiasm for collaborative projects turns a team into a strong unit where every opinion is valued. It is only when expertise, highly professional processes, and enjoyment of work come together that we can shape the future collectively.

We build and operate the ML infrastructure that takes perception and vision models from experiment to production - across a data mesh of domain-owned datasets, through large-scale distributed training on Qualcomm Cloud AI 100 and NVIDIA GPU clusters, all the way to optimized, deployment-ready artefacts for resource-constrained hardware in the vehicle.

Aufgaben

  • You build and maintain end-to-end ML pipelines using workflow, personal and professional development opportunities.
  • Attractive, fair and performance-related remuneration.
  • High level of job security.
  • Annual special payments such as vacation pay, Christmas bonus, and profit sharing.
  • Flexible working hours including six weeks annual leave and overtime compensation.
  • Discounted BMW & MINI conditions.
  • Many other benefits at bmw.jobs/benefits

Earliest starting date: from now on

Type of employment: unlimited

Working hours: full-time

At the BMW Group, we place great importance on equal treatment and equal opportunities. Our recruiting decisions are based on the personality, experience, and skills of the applicants. Learn more here.

JBFD1_DE

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on jobfinder.de

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

5:28 min

Defining MLOps and its role in production systems

Hauke Brammer · WWC 2023

1:34 min

Essential commands for running and testing Terraform configurations

Hennie Francis · LIVE

5:37 min

Extensibility and programmability features of the PostgreSQL database

Silvano Coriani Silvano Coriani · Europe 2026 Virtual

2:28 min

Understanding Kubernetes architecture and core cluster components

Marc Nimmerrichter · WWC 2022

2:44 min

Defining core roles and responsibilities in MLOps teams

Bas Geerdink · LIVE

2:32 min

Overview of Terraform and Terraform Cloud features

Devlin Duldulao · LIVE

Videos

See all

Related articles

See all